It is widely agreed that R is not a fast language. Notably, because it is an interpreted language. To overcome this issue, some solutions exists which allow to compile functions written in R. The gains in computation time can be considerable. But it depends on our ability to write code that can benefit from these tools.
In this tutorial, we study the efficiency of the Luke Tierney's “compiler” package which is provided in the base distribution of R. We program two standard data analysis treatments, (1) with and (2) without using loops: the scaling of variables in a data frame; the calculation of a correlation matrix by matrix product. We compare the efficiency of non-compiled and compiled versions of these functions.
We observe that the gain for the compiled version is dramatic for the version with loops, but negligible for the second variant. We note also that, in the R 3.4.2 version used, it is not needed to compile explicitly the functions containing loops because it exists a JIT (just in time compilation) mechanism which ensure to our code the maximal performance.
Keywords: package compiler, cmpfun, byte code, package rbenchmark, benchmark, JIT, just in time
Tutorial: en_Tanagra_R_compiler_package.pdf
Program: compilation_r.zip
References :
Luke Tierney, "A Byte Code Compiler for R", Department of Statistics and Actuarial Science, University of Iowa, March 30, 2012.
Package 'compiler' - "Byte Code Compiler"
